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Beagrie, R.A.* ; Thieme, C.J.* ; Annunziatella, C.* ; Baugher, C.* ; Zhang, Y.* ; Schueler, M.* ; Kukalev, A.* ; Kempfer, R.* ; Chiariello, A.M.* ; Bianco, S.* ; Li, Y.* ; Davis, T.* ; Scialdone, A. ; Welch, L.R.* ; Nicodemi, M.* ; Pombo, A.*

Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C.

Nat. Methods 20, 1037-1047 (2023)

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Open Access Green as soon as Postprint is submitted to ZB.

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Technology for measuring 3D genome topology is increasingly important for studying gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of genome architecture mapping (GAM), a ligation-free technique that maps chromatin contacts genome-wide. We perform a detailed comparison of multiplex-GAM and Hi-C using mouse embryonic stem cells. When examining the strongest contacts detected by either method, we find that only one-third of these are shared. The strongest contacts specifically found in GAM often involve ‘active’ regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain ‘inactive’ regions. Our work shows that active genomic regions are involved in extensive complex contacts that are currently underestimated in ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Rna-polymerase-ii; Organization; Reveals; Principles; Dynamics

ISSN (print) / ISBN 1548-7091

e-ISSN 1548-7105

Journal Nature Methods

Quellenangaben Volume: 20, Issue: 7, Pages: 1037-1047

Publisher Nature Publishing Group

Publishing Place New York, NY

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Epigenetics and Stem Cells (IES)
Institute of Functional Epigenetics (IFE)
Institute of Computational Biology (ICB)

Grants NIDDK NIH HHS
Wellcome Trust